Distribution, relationship, and environmental driving factors of chlorophyll-a and algal cell density: A national view of China

Global Ecology and Conservation, Journal Year: 2024, Volume and Issue: 54, P. e03084 - e03084

Published: July 11, 2024

Chlorophyll-a (Chl-a) and algal cell density (ACD) are vital for assessing proliferation eutrophication in aquatic ecosystems. Although Chl-a is often used as a proxy ACD, its accuracy requires validation, studies on their linear correlation scarce. Additionally, ACD influenced by various factors, including nutritional, economic, biochemical, physicochemical, meteorological factors. However, the relative importance of these factors remains insufficiently studied. This study analyzed data from 57 lakes reservoirs across China March 2021 to February 2023, investigating distribution patterns different regions seasons. The employed regression model explore between seasons throughout China. Furthermore, Mantel test generalized were evaluated nutritional (total nitrogen (TN), total phosphorus (TP), TN/TP ratio (TN/TP), ammonia (NH3-N)), economic (gross domestic product), (surface pressure, net solar radiation, air temperature, wind speed (WS), rainfall (RF)), well biochemical physicochemical (turbidity (TUR), pH value, water temperature (WT), permanganate index, dissolved oxygen) ACD. Results showed that average concentrations South highest, at 12 μg/L 19.5 × 106 cells/L, respectively. Seasonally, peaked spring was lowest winter, while summer winter. Significant seasonal regional variations observed, with showing strongest relationship. In Central China, significantly correlated four seasons, whereas correlations less distinct Eastern Western regions. Therefore, caution. Nutrient (TN, TP, TN/TP, NH3-N) identified primary drivers Meteorological (WS, RF), along (WT, TUR) also emerged critical predictors spatial variations. validates analyzes spatiotemporal distribution, assesses influence enhancing our understanding dynamics Chinese reservoirs.

Language: Английский

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A novel water quality risk assessment framework for reservoir water bodies coupling key parameter selection and dynamic warning threshold determination

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 24, 2025

Water quality early warning is crucial for protecting ecological security and controlling pollution in lakes reservoirs. However, the traditional level may not provide accurate data a specific area. Therefore, it necessary to design an adaptive threshold identification system that conforms actual operating environment. This study monitored nine water parameters-water temperature (WT), pH, dissolved oxygen (DO), permanganate index (CODMn), chemical demand (COD), five-day biochemical (BOD5), total nitrogen (TN), phosphorus (TP), ammonia (NH3-N)-monthly from 11 sampling sites Danjiangkou Reservoir, i.e., largest artificial lake Asia, 2017 2022. The reservoir was divided into three sub-areas by cluster analysis: Danku, Hanku, intake. Quality Index (WQI) used comprehensive spatiotemporal evaluation, minimum WQI (WQImin) model developed using multiple linear regression. Finally, risk early-warning proposed based on frequency analysis, categorizing six levels. findings reveal each area maintains at "good" or "excellent" levels during period. average values, lowest highest, are Hanku (75.44), Danku (78.78), intake (79.07). result shows of Reservoir has been maintained good due control management Chinese government after operation Middle Route South-to-North Diversion Project China. WQImin models have different key parameters: WT, DO, TN, TP, COD common all areas, whereas NH3-N included both models. BOD5 pH were unique models, respectively. TN TP identified as parameters affecting safety Reservoir. thresholds significantly higher than those intake, indicating worst. These dynamically revised through new became available. assessment framework provides robust tool offers scientific reliable reference administrative departments implement effective environment prevention strategies.

Language: Английский

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Identifying Mechanisms of the Outbreak and Termination of Riverine Diatom Blooms Using Daily Sampling Data

Published: Jan. 1, 2024

Intensive anthropogenic activities and climate change have led to frequent algal blooms in rivers globally, threatening riverine ecosystems. Traditional sampling methods such as monthly or seasonal make causal analysis difficult due multiple influencing factors complex temporal dynamics of algae. This study investigates the mechanisms behind outbreak termination a diatom bloom downstream Han River (DHR) central China using high-frequency daily data from February 27 March 27, 2019. Diatom density peaked at 8.30 × 106 cells/L. Multivariate statistical analyses revealed that increased water stabilization, rising temperatures (up 16.2 °C), high light intensity 139 W/m²) were key triggering bloom, with nutrients not being limiting factors. Conversely, nitrogen levels found significantly correlate during period, indicating limitation (TN reduced 1.216 mg/L) critical factor ending bloom. discrepancy may stem use long-term, low-frequency prior research. The highlights importance monitoring capture rapid dynamics, offering valuable insights for prevention control blooms.

Language: Английский

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A novel coupling interpretable machine learning framework for water quality prediction and environmental effect understanding in different flow discharge regulations of hydro-projects

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 950, P. 175281 - 175281

Published: Aug. 6, 2024

Language: Английский

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Unveiling Portfolio Resilience: Harnessing Asymmetric Copulas for Dynamic Risk Assessment in the Knowledge Economy

Journal of the Knowledge Economy, Journal Year: 2023, Volume and Issue: 15(3), P. 10200 - 10226

Published: Oct. 2, 2023

Language: Английский

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